Monitoring electroencephalography (EEG) signals is an important task for the early diagnosis in seizures. While analyzing EEG signals plays an important role in monitoring the brain activity of patients, an expert is needed to analyze all EEG recordings to detect seizure activity. This can be tedious and time-consuming, and a timely and accurate diagnosis of seizure activity is essential to initiate therapy and reduce the risk of future seizures and seizure-related complications.
At the moment, machine learning algorithms provide an avenue to classify EEG signals and minimize expert intervention. Though machine learning algorithms require good quality EEG signals to provide effective classification results. Oftentimes, the EEG signals provided to machine learning algorithms need to be optimized to make the machine learning algorithms more effective in predicting seizures.